Understanding Privacy Risks of AI Features

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

Before diving headfirst into AI, companies need to define what data privacy means to them in order to use GenAI safely. After decades of harvesting and storing data, many tech companies have created vast troves of the stuff - and not all of it is safe to use when training new GenAI models. Most companies can easily recognize obvious examples of Personally Identifying Information (PII) like Social Security numbers (SSNs) - but what about home addresses, phone numbers, or even information like how many kids a customer has? These details can be just as critical to ensure newly built GenAI products don’t compromise their users' privacy - or safety - but once this information has entered an LLM, it can be really difficult to excise it. To safely build the next generation of AI, companies need to consider some key issues: ⚠️Defining Sensitive Data: Companies need to decide what they consider sensitive beyond the obvious. Personally identifiable information (PII) covers more than just SSNs and contact information - it can include any data that paints a detailed picture of an individual and needs to be redacted to protect customers. 🔒Using Tools to Ensure Privacy: Ensuring privacy in AI requires a range of tools that can help tech companies process, redact, and safeguard sensitive information. Without these tools in place, they risk exposing critical data in their AI models. 🏗️ Building a Framework for Privacy: Redacting sensitive data isn’t just a one-time process; it needs to be a cornerstone of any company’s data management strategy as they continue to scale AI efforts. Since PII is so difficult to remove from an LLM once added, GenAI companies need to devote resources to making sure it doesn’t enter their databases in the first place. Ultimately, AI is only as safe as the data you feed into it. Companies need a clear, actionable plan to protect their customers - and the time to implement it is now.

Interesting article that discusses a newly discovered vulnerability in Slack's AI feature that could allow attackers to exfiltrate sensitive data from private channels. The flaw involves "prompt injection," where an attacker manipulates the context Slack AI uses to process queries, enabling them to trick the AI into generating malicious links or leaking confidential information without needing direct access to the victim's private channels. The vulnerability is demonstrated through two main attack scenarios: 1. Data Exfiltration Attack: An attacker creates a public Slack channel containing a hidden malicious prompt. When a victim queries Slack AI for a stored API key, the AI inadvertently combines the attacker’s hidden instructions with the victim's legitimate data, resulting in a phishing link that sends the API key to the attacker’s server. 2. Phishing Attack: The attacker crafts a message in a public channel referencing someone like the victim’s manager. When the victim queries Slack AI for messages from that person, the AI mixes in the attacker’s content, creating a convincing phishing link. The risk increased following Slack’s August 14th update, which expanded the AI’s ability to ingest content from files. Although the vulnerability was disclosed to Slack, their initial response was underwhelming, prompting researchers to push for public awareness. This vulnerability highlights the persistent risks of integrating generative AI into sensitive environments like Slack. As we add AI capabilities to communication tools, we must be cautious about the potential for adversarial exploitation—especially when it comes to prompt injection attacks. Unlike traditional software bugs, these attacks prey on how AI interprets and combines context, making them more subtle and harder to detect. What’s particularly concerning is how this attack can be carried out without needing direct access to a user’s private data. By simply planting hidden instructions in an obscure public channel, attackers can bypass access controls, showing just how fragile security can be when an AI can’t distinguish between legitimate prompts and malicious inputs. From a practical standpoint, organizations should carefully consider limiting where and how Slack AI is allowed to operate, especially in environments where sensitive data is shared. Additionally, Slack (and other platforms) need to prioritize robust defenses against prompt injection—such as stricter prompt parsing or additional safeguards around context windows—before fully rolling out AI features. Lastly, this incident underscores the importance of responsible disclosure and transparent communication between researchers and companies. Users should be empowered to understand risks, and vendors must be quick to address emerging threats in their AI-driven solutions.

🚨 Using DeepSeek Poses Serious Risks to Your Privacy and Security 🚨 DeepSeek, the AI chatbot developed by a Chinese firm, has gained immense popularity recently. However, beneath its advanced capabilities lie critical security flaws, privacy risks, and potential ties to the Chinese government that make it unsafe for use. Here’s why you should think twice before using DeepSeek: 1. Major Data Breaches and Security Vulnerabilities Exposed Database: DeepSeek recently left over 1 million sensitive records, including chat logs and API keys, openly accessible due to an unsecured database. This exposed user data to potential cyberattacks and espionage. Unencrypted Data Transmission: The DeepSeek iOS app transmits sensitive user and device data without encryption, making it vulnerable to interception by malicious actors. Hardcoded Encryption Keys: Weak encryption practices, such as the use of outdated algorithms and hardcoded keys, further compromise user data security. 2. Ties to the Chinese Government Data Storage in China: DeepSeek stores user data on servers governed by Chinese law, which mandates companies to cooperate with state intelligence agencies. Hidden Code for Data Transmission: Researchers uncovered hidden programming in DeepSeek's code that can transmit user data directly to China Mobile, a state-owned telecommunications company with known ties to the Chinese government. National Security Concerns: U.S. lawmakers and cybersecurity experts have flagged DeepSeek as a tool for potential surveillance, urging bans on its use in government devices. 3. Privacy and Ethical Concerns Extensive Data Collection: DeepSeek collects detailed user information, including chat histories, device data, keystroke patterns, and even activity from other apps. This raises serious concerns about profiling and surveillance. Propaganda Risks: Investigations reveal that DeepSeek's outputs often align with Chinese government narratives, spreading misinformation and censorship on sensitive topics like Taiwan or human rights issues. 4. Dangerous Outputs and Misuse Potential Harmful Content Generation: Studies show that DeepSeek is significantly more likely than competitors to generate harmful or biased content, including extremist material and insecure code. Manipulation Risks: Its vulnerabilities make it easier for bad actors to exploit the platform for phishing scams, disinformation campaigns, and even cyberattacks. What Should You Do? Avoid using DeepSeek for any sensitive or personal information. Advocate for transparency and stricter regulations on AI tools that pose security risks. Stay informed about safer alternatives developed by companies with robust privacy protections. Your data is valuable—don’t let it fall into the wrong hands. Let’s prioritize safety and accountability in AI! 💡

Should we really trust AI to manage our most sensitive healthcare data? It might sound cautious, but here’s why this question is critical: As AI becomes more involved in patient care, the potential risks—especially around privacy and bias—are growing. The stakes are incredibly high when it comes to safeguarding patient data and ensuring fair treatment. The reality? • Patient Privacy Risks – AI systems handle massive amounts of sensitive information. Without rigorous privacy measures, there’s a real risk of compromising patient trust. • Algorithmic Bias – With 80% of healthcare datasets lacking diversity, AI systems may unintentionally reinforce health disparities, leading to skewed outcomes for certain groups. • Diversity in Development – Engaging a range of perspectives ensures AI solutions reflect the needs of all populations, not just a select few. So, what’s the way forward? → Governance & Oversight – Regulatory frameworks must enforce ethical standards in healthcare AI. → Transparent Consent – Patients deserve to know how their data is used and stored. → Inclusive Data Practices – AI needs diverse, representative data to minimize bias and maximize fairness. The takeaway? AI in healthcare offers massive potential, but only if we draw ethical lines that protect privacy and promote inclusivity. Where do you think the line should be drawn? Let’s talk. 👇

I'm increasingly convinced that we need to treat "AI privacy" as a distinct field within privacy, separate from but closely related to "data privacy". Just as the digital age required the evolution of data protection laws, AI introduces new risks that challenge existing frameworks, forcing us to rethink how personal data is ingested and embedded into AI systems. Key issues include: 🔹 Mass-scale ingestion – AI models are often trained on huge datasets scraped from online sources, including publicly available and proprietary information, without individuals' consent. 🔹 Personal data embedding – Unlike traditional databases, AI models compress, encode, and entrench personal data within their training, blurring the lines between the data and the model. 🔹 Data exfiltration & exposure – AI models can inadvertently retain and expose sensitive personal data through overfitting, prompt injection attacks, or adversarial exploits. 🔹 Superinference – AI uncovers hidden patterns and makes powerful predictions about our preferences, behaviours, emotions, and opinions, often revealing insights that we ourselves may not even be aware of. 🔹 AI impersonation – Deepfake and generative AI technologies enable identity fraud, social engineering attacks, and unauthorized use of biometric data. 🔹 Autonomy & control – AI may be used to make or influence critical decisions in domains such as hiring, lending, and healthcare, raising fundamental concerns about autonomy and contestability. 🔹 Bias & fairness – AI can amplify biases present in training data, leading to discriminatory outcomes in areas such as employment, financial services, and law enforcement. To date, privacy discussions have focused on data - how it's collected, used, and stored. But AI challenges this paradigm. Data is no longer static. It is abstracted, transformed, and embedded into models in ways that challenge conventional privacy protections. If "AI privacy" is about more than just the data, should privacy rights extend beyond inputs and outputs to the models themselves? If a model learns from us, should we have rights over it? #AI #AIPrivacy #Dataprivacy #Dataprotection #AIrights #Digitalrights

A lot of companies think they’re “safe” from AI compliance risks simply because they haven’t formally adopted AI. But that’s a dangerous assumption—and it’s already backfiring for some organizations. Here’s what’s really happening— Employees are quietly using ChatGPT, Claude, Gemini, and other tools to summarize customer data, rewrite client emails, or draft policy documents. In some cases, they’re even uploading sensitive files or legal content to get a “better” response. The organization may not have visibility into any of it. This is what’s called Shadow AI—unauthorized or unsanctioned use of AI tools by employees. Now, here’s what a #GRC professional needs to do about it: 1. Start with Discovery: Use internal surveys, browser activity logs (if available), or device-level monitoring to identify which teams are already using AI tools and for what purposes. No blame—just visibility. 2. Risk Categorization: Document the type of data being processed and match it to its sensitivity. Are they uploading PII? Legal content? Proprietary product info? If so, flag it. 3. Policy Design or Update: Draft an internal AI Use Policy. It doesn’t need to ban tools outright—but it should define: • What tools are approved • What types of data are prohibited • What employees need to do to request new tools 4. Communicate and Train: Employees need to understand not just what they can’t do, but why. Use plain examples to show how uploading files to a public AI model could violate privacy law, leak IP, or introduce bias into decisions. 5. Monitor and Adjust: Once you’ve rolled out your first version of the policy, revisit it every 60–90 days. This field is moving fast—and so should your governance. This can happen anywhere: in education, real estate, logistics, fintech, or nonprofits. You don’t need a team of AI engineers to start building good governance. You just need visibility, structure, and accountability. Let’s stop thinking of AI risk as something “only tech companies” deal with. Shadow AI is already in your workplace—you just haven’t looked yet.

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

5 Risks You Must Know About AI And Privacy (Your business depends on trust — but AI can put that trust at risk.) AI loves data, but mishandling it can cost you big. Here are the top 5 privacy risks you need to watch: ↳ Unauthorized Access — Weak controls let hackers or insiders grab sensitive data. ↳ Poor Anonymization — Bad techniques can easily be reversed, exposing identities. ↳ Bias And Discrimination — Biased AI models can create unfair, illegal outcomes. ↳ Data Over-Collection — Grabbing too much data increases breach and legal risks. ↳ Weak Ethical Guardrails — Without checks, your AI can drift into privacy violations. So how do you reduce these risks? Here’s your checklist: ↳ Strong Access Controls ↳ Regular Data Audits ↳ Robust, Irreversible Anonymization ↳ Ethical AI Frameworks To Monitor Bias ↳ Collect Only What You Need Winning with AI is not just about power, it’s about responsibility. __________________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI

AI Models Are Talking, But Are They Saying Too Much? One of the most under-discussed risks in AI is the training data extraction attack, where a model reveals pieces of its training data when carefully manipulated by an adversary through crafted queries. This is not a typical intrusion or external breach. It is a consequence of unintended memorization. A 2023 study by Google DeepMind and Stanford found that even billion-token models could regurgitate email addresses, names, and copyrighted code, just from the right prompts. As models feed on massive, unfiltered datasets, this risk only grows. So how do we keep our AI systems secure and trustworthy? ✅ Sanitize training data to remove sensitive content ✅ Apply differential privacy to reduce memorization ✅ Red-team the model to simulate attacks ✅ Enforce strict governance & acceptable use policies ✅ Monitor outputs to detect and prevent leakage 🔐 AI security isn’t a feature, it’s a foundation for trust. Are your AI systems safe from silent leaks? 👇 Let’s talk AI resilience in the comments. 🔁 Repost to raise awareness 👤 Follow Anand Singh for more on AI, trust, and tech leadership